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Knowledge library

Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.

Quant Q&A
20,364 documents
SuperMind
12,226 documents
OKX Learn
8,431 documents
Strategy library
7,910 documents
MQL5 code base
7,090 documents
BigQuant
3,481 documents
Bitget Academy
3,298 documents
MQL5 articles
3,012 documents
TradingView scripts
1,976 documents
ProRealCode
1,507 documents
Deribit Insights
1,232 documents
Machine Learning for Trading
1,124 documents
arXiv papers
1,033 documents
Amberdata research
766 documents
FMZ forum
682 documents
FMZ digest
662 documents
vn.py community
560 documents
QuantInsti blog
511 documents
Galaxy Research
340 documents
QuantStart
246 documents
Stratmill research code
219 documents
Robot Wealth
195 documents
NautilusTrader
191 documents
Hummingbot docs
181 documents
Paradigm research
175 documents
Lumibot
164 documents
Kraken Learn
163 documents
Quant course library
157 documents
OctoBot
152 documents
Cryptohopper blog
144 documents
Systematic trading blog (Rob Carver)
132 documents
Qlib
116 documents
TqSdk
86 documents
Quantpedia
86 documents
Hyperliquid docs
79 documents
Freqtrade
68 documents
Hudson & Thames
62 documents
Awesome Systematic Trading
61 documents
backtrader
54 documents
vn.py
50 documents
Binance API docs
45 documents
Quantopian lectures
45 documents
FMZ guides
38 documents
pysystemtrade
34 documents
Freqtrade docs
32 documents
quant-trading
31 documents
FinRL
28 documents
Zipline
22 documents
FMZ live strategies
21 documents
Jesse
17 documents
pyfolio
16 documents
Alphalens
14 documents
WonderTrader
14 documents
backtesting.py
11 documents
Technical Analysis
9 documents
QTPyLib
8 documents
QuantRocket
7 documents
Lumibot strategies
7 documents
Awesome Quant
1 documents

Search the library

3,481 documents

BigQuant

A user asks why a BigQuant query for Ping An Bank returns values in the thousands when the stock trades around the teens. The supplied workflow selects the A-share instrument, then builds a forward label from the close five sessions ahead divided by the next…

EquitiesMachine learningChina markets
BigQuant

This report summary examines whether separating overnight returns from intraday returns can improve equity reversal factors in China’s A-share market. It links the market’s T+1 trading convention with a possible low-open, rising-intraday pattern that may…

EquitiesMean reversionFactor investingChina markets
BigQuant

This research summary argues that a fund’s track record can mix the results of successive managers, obscuring changes in style and investment approach. It proposes constructing a separate index for each manager from funds solely attributed to that manager,…

Multi-assetFactor investingStatisticsPortfolio construction
BigQuant

This article argues that training one stock-selection model on all market data may struggle with noisy observations and changing factor behavior. Its proposed approach is to classify market conditions and maintain strategies suited to different regimes:…

EquitiesChina marketsMachine learningBacktesting
BigQuant

This report describes a Chinese equity risk model built on a standard multifactor framework and informed by the Barra approach. It estimates stock return covariance through factor return covariance and specific return variances. The model adjusts these…

EquitiesChina marketsFactor investingRisk management
BigQuant

This post outlines an A-share stock-selection framework that uses eight cross-sectionally ranked features covering valuation, momentum and reversal, and liquidity. A LightGBM regression model predicts relative returns over a five-trading-day horizon; the…

EquitiesMachine learningFactor investingBacktesting
BigQuant

This research summary compares ten equity valuation measures, including book-to-price, earnings-to-price, sales-to-price, dividend yield, growth-adjusted earnings multiples, cash-flow yields, and EBITDA relative to enterprise value. It describes what each…

EquitiesFactor investingStatisticsBacktesting
BigQuant

This note reconstructs a Chinese ETF rotation strategy that selects a small pool of relatively uncorrelated assets, including equity index ETFs and gold. Every five trading days, it compares each fund’s 20-day return, holds the strongest when that return is…

China marketsMulti-assetTrend followingMomentum
BigQuant

This research note examines whether intraday stock market snapshots can support factors for stock selection. Snapshot data records prices, volumes, trade counts, and order-book quotes at regular intervals; the note contrasts it with trade-by-trade data and…

EquitiesHigh-frequency tradingFactor investingMarket microstructure
BigQuant

This overview surveys several ways to rank or select equities using quantitative models. It groups approaches into factor and risk models, statistical methods, machine learning, macroeconomic inputs, and behavioral signals. Examples include combining value,…

EquitiesFactor investingMachine learningStatistics
BigQuant

The study examines whether WTI crude futures trading before weekly US Energy Information Administration inventory releases is consistent with informed trading. It compares announcement outcomes with analyst expectations and analyzes intraday prices and…

FuturesCommoditiesMarket microstructureEvent-driven
BigQuant

The article introduces random forests as an ensemble of decision trees and explains how bootstrap sampling and random feature selection create diversity among trees. It reviews classification and regression trees, impurity measures, pruning, regression…

EquitiesMachine learningFactor investingStatistics
BigQuant

This report summary describes two models for China’s nonferrous metals sector: macroeconomic and industry-data timing for the sector, and multifactor stock selection within it. For timing, it uses mean t-tests to identify candidate indicators for both…

China marketsEquitiesCommoditiesFactor investing
BigQuant

This research review examines whether actively managed U.S. equity funds can generate CAPM alpha without weakening the coskewness of their returns with the market. It explains why investors who value positive skew may care about coskewness alongside…

EquitiesStatisticsRisk managementPortfolio construction
BigQuant

The note introduces quantitative investing as a standardized process that uses data, mathematical rules, and a model to make investment decisions. It contrasts this approach with discretionary stock selection based on personal feelings, describing how a…

StatisticsRisk management
BigQuant

The article sketches a basic intraday T+0 approach for a stock expected to move within a relatively narrow range for some time. It proposes setting a price-amplitude threshold and making frequent small-lot trades to capture gains from oscillations. The…

EquitiesMean reversionExecutionRisk management
BigQuant

This study examines how analyst coverage and earnings forecast revisions relate to the negative association between idiosyncratic volatility (IVOL) and future stock returns in Chinese A-shares. It measures IVOL from residual volatility in a Fama–French…

EquitiesChina marketsVolatilityFactor investing
BigQuant

This weekly report summarizes several Chinese equity strategies for May 2018, including multi-factor index enhancement, short-horizon price-volume, AI stock selection, Smart Beta, event-driven selection, ESG, similarity matching, and an R-breaker trading…

EquitiesChina marketsFactor investingEvent-driven
BigQuant

This announcement describes the launch of a collaborative factor research group and outlines how participants will share and preserve their work. The organizers propose regular progress summaries, periodic research reports, and presentations, with factors…

Factor investingEquitiesBacktesting
BigQuant

This excerpt analyzes how China’s carbon-reduction targets and global renewable deployment could create work for construction companies in power generation, storage, and grid infrastructure. It describes engineering opportunities in wind and solar projects,…

EquitiesChina marketsCommoditiesRisk management
BigQuant

This research summary examines whether intraday data can improve sector-rotation signals, focusing on realized skewness and the share of volatility attributable to downside moves. It describes constructing these measures from high-frequency observations and…

China marketsFactor investingHigh-frequency tradingPortfolio construction
BigQuant

The document reviews Chinese A-share stocks with very large gains from two perspectives. First, it surveys high-return stocks by year since 2009 and considers market conditions, company size, recent listings, and corporate restructuring as possible…

EquitiesFactor investingStatisticsChina markets
BigQuant

The document defines industry leader stocks as companies with strong analyst coverage and fundamentals that are closely related to those of other firms in their industry. It reports that analysts tend to cover more heavily companies whose fundamentals move…

EquitiesFactor investingMomentumStatistics